Table REF  outlines the quantitative comparison of different methods. Specifically, we compared our UCAN with the classical one-to-one fixed translation method (cGAN [1]}), and the unified translation method (StarGAN [2]}). Both NMSE and SSIM are evaluated for any two tracers' translations. Our UCAN without MR (UCAN-MR) achieves slightly better performance compared to the conventional one-to-one cGAN, while using only one model. Our UCAN-MR also demonstrates superior performance as compared to the previous unified StarGAN for all the translation paths. Incorporating anatomy information into UCAN significantly improves the translation performance for all the translation paths. For the most challenging translation paths, such as \(C \rightarrow A\)  and \(C \rightarrow B\) , we reduced the NMSE from \(19.13\)  to \(10.23\)  and \(16.81\)  to \(8.63\) , respectively.
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